{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/c/Users/wqw/Desktop/code/mycode/wangqiwen/jupyter\n",
      "cluster_job.ipynb\n",
      "draw.ipynb\n",
      "model.ipynb\n",
      "scikit-learn-example.ipynb\n",
      "test_pandas_numpy.ipynb\n",
      "text_process.ipynb\n",
      "word2vec.ipynb\n"
     ]
    }
   ],
   "source": [
    "!pwd\n",
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\r\n",
      "  Downloading https://files.pythonhosted.org/packages/10/01/dd1c7838cde3b69b247aaeb61016e238cafd8188a276e366d36aa6bcdab4/seaborn-0.8.1.tar.gz (178kB)\r\n",
      "Building wheels for collected packages: seaborn\r\n",
      "  Running setup.py bdist_wheel for seaborn: started\r\n",
      "  Running setup.py bdist_wheel for seaborn: finished with status 'done'\r\n",
      "  Stored in directory: C:\\Users\\wqw\\AppData\\Local\\pip\\Cache\\wheels\\26\\0a\\44\\53ddd89769e62f7c6691976375b86c6492e7dd20a2d3970e32\r\n",
      "Successfully built seaborn\r\n",
      "Installing collected packages: seaborn\r\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not install packages due to an EnvironmentError: [WinError 5] 拒绝访问。: 'c:\\\\program files (x86)\\\\python\\\\Lib\\\\site-packages\\\\seaborn'\r\n",
      "Consider using the `--user` option or check the permissions.\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "pip install seaborn\n",
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "print('hello')\n",
    "#shift+enter, alt,ctrl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iris数据集简介\n",
    "- iris数据集: 安德森鸢尾花卉数据集，英文全称是Anderson’s Iris data set\n",
    "## 数据格式\n",
    "- iris包含150个样本，每行数据包含四个特征和类别，iris数据集是一个150行5列的二维表\n",
    "   - 4个特征：花萼长度、花萼宽度、花瓣长度、花瓣宽度\n",
    "   - 1个目标：3个类别，山鸢尾、变色鸢尾还是维吉尼亚鸢尾,setosa, versicolor, virginica\n",
    "iris的每个样本都包含了品种信息，即目标属性（第5列，也叫target或label）。\n",
    "- 特征\n",
    "![特征](http://www.bogotobogo.com/python/scikit-learn/images/features/iris-data-set.png)\n",
    "- 目标\n",
    "![目标](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Machine+Learning+R/iris-machinelearning.png)\n",
    "## 示例\n",
    "- 示例\n",
    "\n",
    "| Sepal length| Sepal width| Petal length| Petal width| Species| \n",
    "|---|---|:---:|---:|:---|\n",
    "| 5.1| 3.5| 1.4| 0.2| I. setosa| \n",
    "| 4.9| 3| 1.4| 0.2| I. setosa| \n",
    "| 4.7| 3.2| 1.3| 0.2| I. setosa| \n",
    "| 4.6| 3.1| 1.5| 0.2| I. setosa| \n",
    "| 5| 3.6| 1.4| 0.3| I. setosa| \n",
    "- 局部截图\n",
    "![数据示例](http://datahref.com/wp-content/uploads/2016/06/2016-06-03-12-32-40%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE.png)\n",
    "## 数据分析\n",
    "特征两两组合\n",
    "![相关图](http://datahref.com/wp-content/uploads/2016/06/Iris_dataset_scatterplot.svg_.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "#print iris.data\n",
    "#print iris.target\n",
    "x_data,y = iris.data, iris.target\n",
    "x_name = iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data.shape\n",
    "#x_data.#shift+tab函数方法\n",
    "#x_data#tab自动补全+方法提示\n",
    "#x_name\n",
    "#y.shape\n",
    "#iris.target_names\n",
    "#x_data.head()#pandas结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用make_classification构造1000个样本，每个样本有20个feature\n",
    "#from sklearn.datasets import make_classification\n",
    "#x_data, y = make_classification(1000, n_features=20, n_informative=2, n_redundant=2, n_classes=2, random_state=0)\n",
    "#cols = [i for i in range(20)] + ['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pandas import DataFrame\n",
    "c=np.hstack((x_data, y[:, None]))\n",
    "dh=DataFrame(c,columns=x_name+['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   species  \n",
       "0      0.0  \n",
       "1      0.0  \n",
       "2      0.0  \n",
       "3      0.0  \n",
       "4      0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#存为dataframe格式\n",
    "from pandas import DataFrame # 共用index的Series结构\n",
    "\n",
    "cols = x_name + ['species']\n",
    "#numpy科学计算工具箱\n",
    "import numpy as np\n",
    "df_iris = DataFrame(np.hstack((x_data, y[:, None])),columns = cols)\n",
    "#df = DataFrame(np.hstack((X, y[:, None])),columns = range(20) + [\"Species\"])\n",
    "df_iris[:5]\n",
    "#df_iris.iloc[:5,:3]  #ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepallength', 'sepalwidth', 'petallength', 'petalwidth', 'species'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_col = df_iris.columns#['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)','petal width (cm)', 'classes']\n",
    "#重新命名\n",
    "df_iris.columns = [i.replace(' ','').strip('(cm)') for i in raw_col]\n",
    "#df_iris['sepallength']\n",
    "#df_iris['species']\n",
    "df_iris.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# seaborn用法\n",
    "[用数据可视化直观理解数据--iris数据集为例](https://blog.csdn.net/u013527419/article/details/69567347)\n",
    "\n",
    "比较常用的图表有：\n",
    "1. 查看目标变量的分布。当分布不平衡时，根据评分标准和具体模型的使用不同，可能会严重影响性能。 \n",
    "   - df_iris['species'].value_counts()\n",
    "1. 对 Numerical Variable，可以用 Box Plot 来直观地查看它的分布。 \n",
    "   - sns.boxplot(x='species', y='petallength', data=df_iris)\n",
    "1. 对于坐标类数据，可以用 Scatter Plot 来查看它们的分布趋势和是否有离群点的存在。 \n",
    "   - df_iris.plot(kind='scatter', x='sepallength', y='sepalwidth') \n",
    "   - sns.jointplot(x='sepallength', y='sepalwidth', data=df_iris, size=5)\n",
    "1. 对于分类问题，将数据根据 Label 的不同着不同的颜色绘制出来，这对 Feature 的构造很有帮助。 \n",
    "   - sns.FacetGrid(df_iris,hue='species',size=5).map(plt.scatter,'sepallength','sepalwidth').add_legend()\n",
    "1. 绘制变量之间两两的分布和相关度图表。 \n",
    "   - sns.pairplot(df_iris, hue='species', size=3) \n",
    "   - sns.pairplot(df_iris, hue='species', size=3, diag_kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    50\n",
       "1.0    50\n",
       "0.0    50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ksk0U0ET/DcoejTFam/ERpV2KaLcpL/JCIBJgXPM4ph0yjTnlc7j4oIv5/OGf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x181b712c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.boxplot(x='species', y='sepallength', data=df_iris)\n",
    "ax = sns.stripplot(x=\"species\", y=\"sepallength\", data=df_iris, jitter=True, edgecolor=\"gray\")\n",
    "#plt.grid()\n",
    "#plt.xlabel('x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L/DXXPzjMwcNj9A8O5+I/RJF2p7mPCujuvudZvWkbV9z6GKs3bWNr3/NZhzQl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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18192a0d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_iris.plot(kind='scatter', x='sepallength', y='sepalwidth')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kCYMHD2bgwIFMnTqVfNmyL730EqNHj2bnnXfmhhtu2OG5m266icMOO4xBgwYx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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2121057f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='sepallength', y='sepalwidth', data=df_iris, size=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tpmcmO8zsAGLJwMyOAram3gTMrBRYBhwK3OHuzyesUgWsh2iCMrOtwAHApoT9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\nEREJTMlEREQCUzIREZHA/j8ywXOjN+uwJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21210be9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df_iris,hue='species',size=5).map(plt.scatter,'sepallength','sepalwidth').add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P2VxaNkduZpBh+EPArhHgma/m3c8LnP9Z04fJZXjlwpWWWS6VdfYFpTFHdWKU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\n/jVwxhlnjBs7duynP/nJT376zjvvHJH7GlVVcdVVV53Y0tJy6rhx4yY89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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21210dc85f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_iris, hue='species', size=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "LinAlgError",
     "evalue": "singular matrix",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-f30619594d6a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# sns.kdeplot == kernel density 核密度图（单个变量）\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_iris\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"species\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"petallength\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_iris\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'species'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiag_kind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'kde'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[1;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, size, aspect, dropna, plot_kws, diag_kws, grid_kws)\u001b[0m\n\u001b[0;32m   2061\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"kde\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2062\u001b[0m             \u001b[0mdiag_kws\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"legend\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2063\u001b[1;33m             \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_diag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mdiag_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2064\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2065\u001b[0m     \u001b[1;31m# Maybe plot on the off-diagonals\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mmap_diag\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m   1381\u001b[0m                         \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfixed_color\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1382\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1383\u001b[1;33m                     \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_k\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel_k\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1384\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1385\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clean_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[1;34m(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, **kwargs)\u001b[0m\n\u001b[0;32m    655\u001b[0m         ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,\n\u001b[0;32m    656\u001b[0m                                  \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 657\u001b[1;33m                                  cumulative=cumulative, **kwargs)\n\u001b[0m\u001b[0;32m    658\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    659\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_univariate_kdeplot\u001b[1;34m(data, shade, vertical, kernel, bw, gridsize, cut, clip, legend, ax, cumulative, **kwargs)\u001b[0m\n\u001b[0;32m    282\u001b[0m                               \u001b[1;34m\"only implemented in statsmodels.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    283\u001b[0m                               \"Please install statsmodels.\")\n\u001b[1;32m--> 284\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_scipy_univariate_kde\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m     \u001b[1;31m# Make sure the density is nonnegative\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_scipy_univariate_kde\u001b[1;34m(data, bw, gridsize, cut, clip)\u001b[0m\n\u001b[0;32m    354\u001b[0m     \u001b[1;34m\"\"\"Compute a univariate kernel density estimate using scipy.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    355\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 356\u001b[1;33m         \u001b[0mkde\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgaussian_kde\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw_method\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    357\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    358\u001b[0m         \u001b[0mkde\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgaussian_kde\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\scipy\\stats\\kde.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, dataset, bw_method)\u001b[0m\n\u001b[0;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_bandwidth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw_method\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbw_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpoints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\scipy\\stats\\kde.py\u001b[0m in \u001b[0;36mset_bandwidth\u001b[1;34m(self, bw_method)\u001b[0m\n\u001b[0;32m    497\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 499\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_covariance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    501\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_compute_covariance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\scipy\\stats\\kde.py\u001b[0m in \u001b[0;36m_compute_covariance\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    508\u001b[0m             self._data_covariance = atleast_2d(np.cov(self.dataset, rowvar=1,\n\u001b[0;32m    509\u001b[0m                                                bias=False))\n\u001b[1;32m--> 510\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data_inv_cov\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data_covariance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    511\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    512\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcovariance\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data_covariance\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfactor\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\scipy\\linalg\\basic.py\u001b[0m in \u001b[0;36minv\u001b[1;34m(a, overwrite_a, check_finite)\u001b[0m\n\u001b[0;32m    974\u001b[0m         \u001b[0minv_a\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetri\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpiv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlwork\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlwork\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moverwrite_lu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    975\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 976\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"singular matrix\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    977\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    978\u001b[0m         raise ValueError('illegal value in %d-th argument of internal '\n",
      "\u001b[1;31mLinAlgError\u001b[0m: singular matrix"
     ]
    }
   ],
   "source": [
    "# violinplot 小提琴图，查看密度分布，结合了前面的两个图，并且进行了简化\n",
    "# 数据越稠密越宽，越稀疏越窄\n",
    "sns.violinplot(x=\"species\", y=\"petallength\", data=df_iris, size=6)\n",
    "\n",
    "# sns.kdeplot == kernel density 核密度图（单个变量）\n",
    "sns.FacetGrid(df_iris, hue=\"species\", size=6).map(sns.kdeplot, \"petallength\").add_legend()\n",
    "sns.pairplot(df_iris, hue='species', size=3, diag_kind='kde')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业一\n",
    "- 解决以上bug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0ohF8UlPh3vp9cxbY6MNHs/CUStyPnYZn9g4qVt9uWPZCSpmV4rpRyZsWbJg3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cR1du7c+Vm9TzqBtO7Kuq73NvlLa/KjKIpFUZStiqKsOrVTNcfIQKqzErDNJRT+2DfiRvv6vXD2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\nMW3n/wNxaAD2iNXDRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x212115eea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#使用pairplot去看不同特征维度pair下数据的空间分布状况\n",
    "_ = sns.pairplot(df_iris,vars=df_iris.columns,hue=\"species\", size=1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "boxplot() got multiple values for argument 'x'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-f429e54683e2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 画各特征之间的相关性\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_iris\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'sepallength'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'species'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: boxplot() got multiple values for argument 'x'"
     ]
    }
   ],
   "source": [
    "# 画各特征之间的相关性\n",
    "plt.figure(figsize=(10, 10))\n",
    "_ = sns.boxplot(df_iris,x='sepallength',y='species')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业二\n",
    "- 解决以上bug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZWR3n3CYza0wwATrXOfdj/muPRtiKjnOuUeiFj3fOHcgX4PFFeWERERHxn1BSM/kIpzcA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x181b9b958d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#f, ax = plt.subplots(figsize=(10, 7))\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.xticks(rotation='90')\n",
    "sns.heatmap(df_iris.corr(), square=True, linewidths=.5, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- seaborn更多用法参考：[Python数据可视化-seaborn](https://www.cnblogs.com/onemorepoint/p/8306885.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 原始数据： \n",
      "     0  1  2\n",
      "0  12  4  1\n",
      "1  13  5  2\n",
      "2  18  6  1\n",
      "3  20  6  2\n",
      "4  14  5  1\n",
      "\n",
      " normalize方法（正则化到单位向量）：\n",
      "           0         1         2\n",
      "0  0.945732  0.315244  0.078811\n",
      "1  0.923870  0.355335  0.142134\n",
      "2  0.947368  0.315789  0.052632\n",
      "3  0.953463  0.286039  0.095346\n",
      "4  0.939618  0.335578  0.067116\n",
      "\n",
      " scale方法（标准正态分布）：\n",
      "           0         1         2\n",
      "0 -1.106606 -1.603567 -0.816497\n",
      "1 -0.781133 -0.267261  1.224745\n",
      "2  0.846228  1.069045 -0.816497\n",
      "3  1.497172  1.069045  1.224745\n",
      "4 -0.455661 -0.267261 -0.816497\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据标准化\n",
    "from sklearn import preprocessing\n",
    "import pandas as pd\n",
    "#iris_normalize = preprocessing.normalize(iris.data)\n",
    "#iris_standardize = preprocessing.scale(iris.data)\n",
    "x=[[12,4,1],[13,5,2],[18,6,1],[20,6,2],[14,5,1]]\n",
    "print('\\n 原始数据： \\n',pd.DataFrame(x))\n",
    "iris_normalize = preprocessing.normalize(x)\n",
    "print('\\n normalize方法（正则化到单位向量）：\\n',pd.DataFrame(iris_normalize))\n",
    "iris_standardize = preprocessing.scale(x)\n",
    "print('\\n scale方法（标准正态分布）：\\n',pd.DataFrame(iris_standardize))\n",
    "iris_standardize.mean(axis=0)\n",
    "iris_standardize.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        50\n",
      "          1       0.98      0.90      0.94        50\n",
      "          2       0.91      0.98      0.94        50\n",
      "\n",
      "avg / total       0.96      0.96      0.96       150\n",
      "\n",
      "[[50  0  0]\n",
      " [ 0 45  5]\n",
      " [ 0  1 49]]\n"
     ]
    }
   ],
   "source": [
    "# 罗辑回归示例\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# 使用鸢尾花数据集\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "model = LogisticRegression()\n",
    "model.fit(X, y)\n",
    "print(model)\n",
    "# make predictions\n",
    "expected = y\n",
    "predicted = model.predict(X) #训练集和测试集要区分开！\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))\n",
    "#print('=====')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业三\n",
    "- 矫正以上错误！训练集+测试集要分开"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB(priors=None)\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        50\n",
      "          1       0.94      0.94      0.94        50\n",
      "          2       0.94      0.94      0.94        50\n",
      "\n",
      "avg / total       0.96      0.96      0.96       150\n",
      "\n",
      "[[50  0  0]\n",
      " [ 0 47  3]\n",
      " [ 0  3 47]]\n"
     ]
    }
   ],
   "source": [
    "# 朴素贝叶斯示例\n",
    "from sklearn import metrics\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB()\n",
    "model.fit(X, y)\n",
    "print(model)\n",
    "# make predictions\n",
    "expected = y\n",
    "predicted = model.predict(X)\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
      "           weights='uniform')\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        50\n",
      "          1       0.96      0.94      0.95        50\n",
      "          2       0.94      0.96      0.95        50\n",
      "\n",
      "avg / total       0.97      0.97      0.97       150\n",
      "\n",
      "[[50  0  0]\n",
      " [ 0 47  3]\n",
      " [ 0  2 48]]\n"
     ]
    }
   ],
   "source": [
    "# K近邻示例\n",
    "from sklearn import metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# fit a k-nearest neighbor model to the data\n",
    "model = KNeighborsClassifier()\n",
    "model.fit(X, y)\n",
    "print(model)\n",
    "# make predictions\n",
    "expected = y\n",
    "predicted = model.predict(X)\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best')\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        50\n",
      "          1       1.00      1.00      1.00        50\n",
      "          2       1.00      1.00      1.00        50\n",
      "\n",
      "avg / total       1.00      1.00      1.00       150\n",
      "\n",
      "[[50  0  0]\n",
      " [ 0 50  0]\n",
      " [ 0  0 50]]\n"
     ]
    }
   ],
   "source": [
    "# 决策树示例\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# fit a CART model to the data\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X, y)\n",
    "print(model)\n",
    "# make predictions\n",
    "expected = y\n",
    "predicted = model.predict(X)\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        50\n",
      "          1       1.00      0.96      0.98        50\n",
      "          2       0.96      1.00      0.98        50\n",
      "\n",
      "avg / total       0.99      0.99      0.99       150\n",
      "\n",
      "[[50  0  0]\n",
      " [ 0 48  2]\n",
      " [ 0  0 50]]\n"
     ]
    }
   ],
   "source": [
    "# 支持向量机分类示例\n",
    "from sklearn import metrics\n",
    "from sklearn.svm import SVC\n",
    "# fit a SVM model to the data\n",
    "model = SVC()\n",
    "model.fit(X, y)\n",
    "print(model)\n",
    "# make predictions\n",
    "expected = y\n",
    "predicted = model.predict(X)\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=None, error_score='raise',\n",
      "       estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
      "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
      "       fit_params={}, iid=True, n_jobs=1,\n",
      "       param_grid={'alpha': array([  1.00000e+00,   1.00000e-01,   1.00000e-02,   1.00000e-03,\n",
      "         1.00000e-04,   0.00000e+00])},\n",
      "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)\n",
      "0.0\n",
      "1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files (x86)\\python\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "c:\\program files (x86)\\python\\lib\\site-packages\\sklearn\\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 正则化参数寻优\n",
    "import numpy as np\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "# prepare a range of alpha values to test\n",
    "alphas = np.array([1,0.1,0.01,0.001,0.0001,0])\n",
    "# create and fit a ridge regression model, testing each alpha\n",
    "model = Ridge()\n",
    "grid = GridSearchCV(estimator=model, param_grid=dict(alpha=alphas))\n",
    "grid.fit(X, y)\n",
    "print(grid)\n",
    "# summarize the results of the grid search\n",
    "print(grid.best_score_)\n",
    "print(grid.best_estimator_.alpha)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n",
      "Estimated number of clusters: 3\n",
      "Homogeneity: 0.953\n",
      "Completeness: 0.883\n",
      "V-measure: 0.917\n",
      "Adjusted Rand Index: 0.952\n",
      "Adjusted Mutual Information: 0.883\n",
      "Silhouette Coefficient: 0.626\n"
     ]
    },
    {
     "data": {
      "image/png": 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I4crCRDWE25pSWblsRcBrbt+5g6kk+HXNVJ3Ajp0fBh01A3jikXXMbBjqIrxqdBPL+YjX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2sNSa/FjQeHQyiFcGdmZiJorj8ubB/K6AO5ifsO8BMmqcyelEhI2HjvQ+BIW1pTVYmExKlLDxCZ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mKf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\nB6NSUfIEfEwUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x274e0680080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DBSCAN算法\n",
    "print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn import metrics\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "##############################################################################\n",
    "# Generate sample data\n",
    "centers = [[1, 1], [-1, -1], [1, -1]]\n",
    "X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,\n",
    "                            random_state=0)\n",
    "\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "##############################################################################\n",
    "# Compute DBSCAN\n",
    "db = DBSCAN(eps=0.3, min_samples=10).fit(X)\n",
    "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
    "core_samples_mask[db.core_sample_indices_] = True\n",
    "labels = db.labels_\n",
    "\n",
    "# Number of clusters in labels, ignoring noise if present.\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "\n",
    "print('Estimated number of clusters: %d' % n_clusters_)\n",
    "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n",
    "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n",
    "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n",
    "print(\"Adjusted Rand Index: %0.3f\"\n",
    "      % metrics.adjusted_rand_score(labels_true, labels))\n",
    "print(\"Adjusted Mutual Information: %0.3f\"\n",
    "      % metrics.adjusted_mutual_info_score(labels_true, labels))\n",
    "print(\"Silhouette Coefficient: %0.3f\"\n",
    "      % metrics.silhouette_score(X, labels))\n",
    "\n",
    "##############################################################################\n",
    "# Plot result\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Black removed and is used for noise instead.\n",
    "unique_labels = set(labels)\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))\n",
    "for k, col in zip(unique_labels, colors):\n",
    "    if k == -1:\n",
    "        # Black used for noise.\n",
    "        col = 'k'\n",
    "\n",
    "    class_member_mask = (labels == k)\n",
    "\n",
    "    xy = X[class_member_mask & core_samples_mask]\n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,\n",
    "             markeredgecolor='k', markersize=14)\n",
    "\n",
    "    xy = X[class_member_mask & ~core_samples_mask]\n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,\n",
    "             markeredgecolor='k', markersize=6)\n",
    "\n",
    "plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始读取数据\n"
     ]
    },
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\warren\\\\Desktop\\\\warren.xlsx'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-9054c3ce7baa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m'开始读取数据'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# 数据格式：time lon lat adcode geohash\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'C:\\Users\\warren\\Desktop\\warren.xlsx'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;31m#df.head()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m'提取经纬度信息'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\program files (x86)\\python\\python27\\lib\\site-packages\\pandas\\io\\excel.pyc\u001b[0m in \u001b[0;36mread_excel\u001b[1;34m(io, sheetname, header, skiprows, skip_footer, index_col, names, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, has_index_names, converters, engine, squeeze, **kwds)\u001b[0m\n\u001b[0;32m    168\u001b[0m     \"\"\"\n\u001b[0;32m    169\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 170\u001b[1;33m         \u001b[0mio\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    171\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m     return io._parse_excel(\n",
      "\u001b[1;32md:\\program files (x86)\\python\\python27\\lib\\site-packages\\pandas\\io\\excel.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, io, **kwds)\u001b[0m\n\u001b[0;32m    225\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 227\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    228\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    229\u001b[0m             raise ValueError('Must explicitly set engine if not passing in'\n",
      "\u001b[1;32md:\\program files (x86)\\python\\python27\\lib\\site-packages\\xlrd\\__init__.pyc\u001b[0m in \u001b[0;36mopen_workbook\u001b[1;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[0;32m    393\u001b[0m         \u001b[0mpeek\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile_contents\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 395\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    396\u001b[0m             \u001b[0mpeek\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    397\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mpeek\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34mb\"PK\\x03\\x04\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# a ZIP file\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\warren\\\\Desktop\\\\warren.xlsx'"
     ]
    }
   ],
   "source": [
    "# encoding=utf-8\n",
    "import pylab as pl\n",
    "from collections import defaultdict,Counter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "\n",
    "# 读取数据 D:\\work\\用户建模画像\\家公司挖掘\\code\\warren.xls\n",
    "print '开始读取数据'\n",
    "# 数据格式：time lon lat adcode geohash\n",
    "df = pd.read_excel('C:\\Users\\warren\\Desktop\\warren.xlsx')\n",
    "#df.head()\n",
    "print '提取经纬度信息'\n",
    "data = df.iloc[:,[1,2]] # 提取经纬度信息\n",
    "print data\n",
    "exit()\n",
    "#points = data.values.tolist()\n",
    "points = data[:100].values.tolist()\n",
    "#x=raw_input()\n",
    "\n",
    "points = [[int(eachpoint.split(\"#\")[0]), int(eachpoint.split(\"#\")[1])] for eachpoint in open(\"points\",\"r\")]\n",
    "# 计算每个数据点相邻的数据点，邻域定义为以该点为中心以边长为2*EPs的网格\n",
    "Eps = 10\n",
    "surroundPoints = defaultdict(list)\n",
    "for idx1,point1 in enumerate(points):\n",
    "  for idx2,point2 in enumerate(points):\n",
    "    if (idx1 < idx2):\n",
    "      if(abs(point1[0]-point2[0])<=Eps and abs(point1[1]-point2[1])<=Eps):\n",
    "        surroundPoints[idx1].append(idx2)\n",
    "        surroundPoints[idx2].append(idx1)\n",
    "# 定义邻域内相邻的数据点的个数大于4的为核心点\n",
    "MinPts = 5\n",
    "corePointIdx = [pointIdx for pointIdx,surPointIdxs in surroundPoints.iteritems() if len(surPointIdxs)>=MinPts]\n",
    "# 邻域内包含某个核心点的非核心点，定义为边界点\n",
    "borderPointIdx = []\n",
    "for pointIdx,surPointIdxs in surroundPoints.iteritems():\n",
    "  if (pointIdx not in corePointIdx):\n",
    "    for onesurPointIdx in surPointIdxs:\n",
    "      if onesurPointIdx in corePointIdx:\n",
    "        borderPointIdx.append(pointIdx)\n",
    "        break\n",
    "# 噪音点既不是边界点也不是核心点\n",
    "noisePointIdx = [pointIdx for pointIdx in range(len(points)) if pointIdx not in corePointIdx and pointIdx not in borderPointIdx]\n",
    "corePoint = [points[pointIdx] for pointIdx in corePointIdx] \n",
    "borderPoint = [points[pointIdx] for pointIdx in borderPointIdx]\n",
    "noisePoint = [points[pointIdx] for pointIdx in noisePointIdx]\n",
    "# pl.plot([eachpoint[0] for eachpoint in corePoint], [eachpoint[1] for eachpoint in corePoint], 'or')\n",
    "# pl.plot([eachpoint[0] for eachpoint in borderPoint], [eachpoint[1] for eachpoint in borderPoint], 'oy')\n",
    "# pl.plot([eachpoint[0] for eachpoint in noisePoint], [eachpoint[1] for eachpoint in noisePoint], 'ok')\n",
    "groups = [idx for idx in range(len(points))]\n",
    "# 各个核心点与其邻域内的所有核心点放在同一个簇中\n",
    "for pointidx,surroundIdxs in surroundPoints.iteritems():\n",
    "  for oneSurroundIdx in surroundIdxs:\n",
    "    if (pointidx in corePointIdx and oneSurroundIdx in corePointIdx and pointidx < oneSurroundIdx):\n",
    "      for idx in range(len(groups)):\n",
    "        if groups[idx] == groups[oneSurroundIdx]:\n",
    "          groups[idx] = groups[pointidx]\n",
    "# 边界点跟其邻域内的某个核心点放在同一个簇中\n",
    "for pointidx,surroundIdxs in surroundPoints.iteritems():\n",
    "  for oneSurroundIdx in surroundIdxs:\n",
    "    if (pointidx in borderPointIdx and oneSurroundIdx in corePointIdx):\n",
    "      groups[pointidx] = groups[oneSurroundIdx]\n",
    "      break\n",
    "# 取簇规模最大的5个簇\n",
    "wantGroupNum = 3\n",
    "finalGroup = Counter(groups).most_common(3)\n",
    "finalGroup = [onecount[0] for onecount in finalGroup]\n",
    "group1 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[0]]\n",
    "group2 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[1]]\n",
    "group3 = [points[idx] for idx in xrange(len(points)) if groups[idx]==finalGroup[2]]\n",
    "pl.plot([eachpoint[0] for eachpoint in group1], [eachpoint[1] for eachpoint in group1], 'or')\n",
    "pl.plot([eachpoint[0] for eachpoint in group2], [eachpoint[1] for eachpoint in group2], 'oy')\n",
    "pl.plot([eachpoint[0] for eachpoint in group3], [eachpoint[1] for eachpoint in group3], 'og')\n",
    "# 打印噪音点，黑色\n",
    "pl.plot([eachpoint[0] for eachpoint in noisePoint], [eachpoint[1] for eachpoint in noisePoint], 'ok')  \n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "min_samples_split must be an integer greater than 1 or a float in (0.0, 1.0]; got the integer 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-33eeacc189b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensemble\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGradientBoostingRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     33\u001b[0m \u001b[0mmse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"MSE: %.4f\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mmse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[0;32m   1032\u001b[0m         \u001b[1;31m# fit the boosting stages\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1033\u001b[0m         n_stages = self._fit_stages(X, y, y_pred, sample_weight, random_state,\n\u001b[1;32m-> 1034\u001b[1;33m                                     begin_at_stage, monitor, X_idx_sorted)\n\u001b[0m\u001b[0;32m   1035\u001b[0m         \u001b[1;31m# change shape of arrays after fit (early-stopping or additional ests)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1036\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mn_stages\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py\u001b[0m in \u001b[0;36m_fit_stages\u001b[1;34m(self, X, y, y_pred, sample_weight, random_state, begin_at_stage, monitor, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1087\u001b[0m             y_pred = self._fit_stage(i, X, y, y_pred, sample_weight,\n\u001b[0;32m   1088\u001b[0m                                      \u001b[0msample_mask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1089\u001b[1;33m                                      X_csc, X_csr)\n\u001b[0m\u001b[0;32m   1090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1091\u001b[0m             \u001b[1;31m# track deviance (= loss)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py\u001b[0m in \u001b[0;36m_fit_stage\u001b[1;34m(self, i, X, y, y_pred, sample_weight, sample_mask, random_state, X_idx_sorted, X_csc, X_csr)\u001b[0m\n\u001b[0;32m    786\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    787\u001b[0m                 tree.fit(X, residual, sample_weight=sample_weight,\n\u001b[1;32m--> 788\u001b[1;33m                          check_input=False, X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m    789\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    790\u001b[0m             \u001b[1;31m# update tree leaves\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1122\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1123\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1124\u001b[1;33m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m   1125\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    192\u001b[0m                                  \u001b[1;34m\"greater than 1 or a float in (0.0, 1.0]; \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    193\u001b[0m                                  \u001b[1;34m\"got the integer %s\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 194\u001b[1;33m                                  % self.min_samples_split)\n\u001b[0m\u001b[0;32m    195\u001b[0m             \u001b[0mmin_samples_split\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin_samples_split\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    196\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# float\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: min_samples_split must be an integer greater than 1 or a float in (0.0, 1.0]; got the integer 1"
     ]
    }
   ],
   "source": [
    "# encoding:utf8\n",
    "# GBDT demo，详见：http://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html#example-ensemble-plot-gradient-boosting-regression-py\n",
    "print(__doc__)\n",
    "\n",
    "# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>\n",
    "#\n",
    "# License: BSD 3 clause\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import ensemble\n",
    "from sklearn import datasets\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "###############################################################################\n",
    "# Load data\n",
    "boston = datasets.load_boston()\n",
    "X, y = shuffle(boston.data, boston.target, random_state=13) # 样本数据混排,\n",
    "X = X.astype(np.float32)\n",
    "offset = int(X.shape[0] * 0.9)\n",
    "X_train, y_train = X[:offset], y[:offset]\n",
    "X_test, y_test = X[offset:], y[offset:]\n",
    "\n",
    "###############################################################################\n",
    "# Fit regression model\n",
    "params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 1,\n",
    "          'learning_rate': 0.01, 'loss': 'ls'}\n",
    "clf = ensemble.GradientBoostingRegressor(**params)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "mse = mean_squared_error(y_test, clf.predict(X_test))\n",
    "print(\"MSE: %.4f\" % mse)\n",
    "\n",
    "###############################################################################\n",
    "# Plot training deviance\n",
    "\n",
    "# compute test set deviance\n",
    "test_score = np.zeros((params['n_estimators'],), dtype=np.float64)\n",
    "\n",
    "for i, y_pred in enumerate(clf.staged_predict(X_test)):\n",
    "    test_score[i] = clf.loss_(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('Deviance')\n",
    "plt.plot(np.arange(params['n_estimators']) + 1, clf.train_score_, 'b-',\n",
    "         label='Training Set Deviance')\n",
    "plt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-',\n",
    "         label='Test Set Deviance')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('Boosting Iterations')\n",
    "plt.ylabel('Deviance')\n",
    "\n",
    "###############################################################################\n",
    "# Plot feature importance\n",
    "feature_importance = clf.feature_importances_\n",
    "# make importances relative to max importance\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, boston.feature_names[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()\n",
    "        "
   ]
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